code: def model_creator_keras(config):
      """
      Keras(tf2) customized model creator
      """
      from bigdl.nano.tf.keras import Sequential
  
      model = Sequential(
          [
              tf.keras.layers.Input(
                  shape=(
                      config["past_seq_len"],
                      config["input_feature_num"],
                  )
              ),
              tf.keras.layers.Dense(
                  config["hidden_dim"], activation="relu"
              ),
              tf.keras.layers.Dropout(config["dropout"]),
              tf.keras.layers.Dense(
                  config["output_feature_num"], activation="softmax"
              ),
          ]
      )
      learning_rate = config.get("lr", 1e-3)
      optimizer = getattr(
          tf.keras.optimizers, config.get("optim", "Adam")
      )(learning_rate)
      model.compile(
          loss=config.get("loss", "mse"),
          optimizer=optimizer,
          metrics=[config.get("metric", "mse")],
      )
      return model
  
imports: from unittest import TestCase
  import pytest
  from bigdl.chronos.utils import LazyImport
  import numpy
  from bigdl.chronos.data import TSDataset
  import pandas
  from ..None import op_torch, op_tf2, op_distributed, op_inference
label_desc: Use image node_id 6 for calling a global function with example usage: model_creator_keras(config) and returns: model